library(tidyverse) # for graphing and data cleaning
library(gardenR) # for Lisa's garden data
library(lubridate) # for date manipulation
library(ggthemes) # for even more plotting themes
library(geofacet) # for special faceting with US map layout
theme_set(theme_minimal()) # My favorite ggplot() theme :)
# Lisa's garden data
data("garden_harvest")
# Seeds/plants (and other garden supply) costs
data("garden_spending")
# Planting dates and locations
data("garden_planting")
# Tidy Tuesday data
kids <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-15/kids.csv')
Before starting your assignment, you need to get yourself set up on GitHub and make sure GitHub is connected to R Studio. To do that, you should read the instruction (through the “Cloning a repo” section) and watch the video here. Then, do the following (if you get stuck on a step, don’t worry, I will help! You can always get started on the homework and we can figure out the GitHub piece later):
keep_md: TRUE in the YAML heading. The .md file is a markdown (NOT R Markdown) file that is an interim step to creating the html file. They are displayed fairly nicely in GitHub, so we want to keep it and look at it there. Click the boxes next to these two files, commit changes (remember to include a commit message), and push them (green up arrow).Put your name at the top of the document.
For ALL graphs, you should include appropriate labels.
Feel free to change the default theme, which I currently have set to theme_minimal().
Use good coding practice. Read the short sections on good code with pipes and ggplot2. This is part of your grade!
When you are finished with ALL the exercises, uncomment the options at the top so your document looks nicer. Don’t do it before then, or else you might miss some important warnings and messages.
These exercises will reiterate what you learned in the “Expanding the data wrangling toolkit” tutorial. If you haven’t gone through the tutorial yet, you should do that first.
garden_harvest data to find the total harvest weight in pounds for each vegetable and day of week (HINT: use the wday() function from lubridate). Display the results so that the vegetables are rows but the days of the week are columns.garden_harvest %>%
mutate(day = wday(date, label = TRUE)) %>%
group_by(vegetable, day) %>%
summarize(total_wt = sum(weight)) %>%
pivot_wider(names_from = day,
values_from = total_wt)
garden_harvest data to find the total harvest in pound for each vegetable variety and then try adding the plot from the garden_planting table. This will not turn out perfectly. What is the problem? How might you fix it?garden_harvest %>%
group_by(vegetable, variety) %>%
summarize(tot_harvest_lb = weight*0.0022) %>%
left_join(plant_date_loc,
by = c("vegetable", "variety"))
## Error in is.data.frame(y): object 'plant_date_loc' not found
Theres missing data and therefore there is a missing error and an unknown object. The inner join function would fix this.
garden_harvest and garden_spending datasets, along with data from somewhere like this to answer this question. You can answer this in words, referencing various join functions. You don’t need R code but could provide some if it’s helpful.This can be done by calculating how many seeds were used for each vegetable in the garden harvest set and pairing it with the supply costs data set. This will give us the specific prices on supplies.
garden_harvest %>%
filter(vegetable == "tomatoes") %>%
mutate(variety = fct_reorder(variety, date, min)) %>%
group_by(variety) %>%
summarize(tot_harvest_lb = sum(weight*0.0022),
min_date = min(date)) %>%
ggplot(aes(x = tot_harvest_lb, y = fct_rev(variety))) +
geom_col(fill = "tomato4")+
labs(title = "Tomato Varieties and Respective Harvest Weight
From Earliest to Latest First Harvest Date",
y = "",
x = "total pounds")
garden_harvest data, create two new variables: one that makes the varieties lowercase and another that finds the length of the variety name. Arrange the data by vegetable and length of variety name (smallest to largest), with one row for each vegetable variety. HINT: use str_to_lower(), str_length(), and distinct().garden_harvest %>%
mutate(lowercase = str_to_lower(variety),
length = str_length(variety)) %>%
group_by(vegetable, variety) %>%
summarize(length = mean(length)) %>%
arrange(vegetable, length)
garden_harvest data, find all distinct vegetable varieties that have “er” or “ar” in their name. HINT: str_detect() with an “or” statement (use the | for “or”) and distinct().garden_harvest %>%
mutate(has_er_ar = str_detect(variety, "er|ar")) %>%
filter(has_er_ar == TRUE) %>%
distinct(vegetable, variety)
In this activity, you’ll examine some factors that may influence the use of bicycles in a bike-renting program. The data come from Washington, DC and cover the last quarter of 2014.
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Two data tables are available:
Trips contains records of individual rentalsStations gives the locations of the bike rental stationsHere is the code to read in the data. We do this a little differently than usualy, which is why it is included here rather than at the top of this file. To avoid repeatedly re-reading the files, start the data import chunk with {r cache = TRUE} rather than the usual {r}.
data_site <-
"https://www.macalester.edu/~dshuman1/data/112/2014-Q4-Trips-History-Data-Small.rds"
Trips <- readRDS(gzcon(url(data_site)))
Stations<-read_csv("http://www.macalester.edu/~dshuman1/data/112/DC-Stations.csv")
NOTE: The Trips data table is a random subset of 10,000 trips from the full quarterly data. Start with this small data table to develop your analysis commands. When you have this working well, you should access the full data set of more than 600,000 events by removing -Small from the name of the data_site.
It’s natural to expect that bikes are rented more at some times of day, some days of the week, some months of the year than others. The variable sdate gives the time (including the date) that the rental started. Make the following plots and interpret them:
sdate. Use geom_density().Trips %>%
ggplot(aes(x = sdate))+
geom_density()+
labs(title = "Distribution of Bike Rentals by Date",
x = "",
y = "")
This shows bike rentals as time moves on. The peak time period was between late October to the start of November. Also as the weather got bad the rentals slowed down from december to january.
mutate() with lubridate’s hour() and minute() functions to extract the hour of the day and minute within the hour from sdate. Hint: A minute is 1/60 of an hour, so create a variable where 3:30 is 3.5 and 3:45 is 3.75.Trips %>%
mutate(hour = hour(sdate),
minute = minute(sdate),
time = (hour + (minute/60))) %>%
ggplot(aes(x = time))+
geom_density()+
labs(title = "Distribution of Bike Rentals by Time of Day",
x = "hour of the day",
y = "")
This plot shows bike rentals to the time of day. the bike renatls see a spike when everyone is awake early in the morning and a decrease when the work day is usually done around 5:30pm.
Trips %>%
mutate(wday = wday(sdate, label = TRUE)) %>%
ggplot(aes(y = fct_rev(wday)))+
geom_bar()+
labs(title = "Bike Rentals by Day of the Week",
x = "",
y = "")
This bar Plot shows bike rentals compared to every day of the week. the most popular day for bike rentals is Friday. Also the weekdays all have more rentals than Saturday and Sunday.
Trips %>%
mutate(hour = hour(sdate),
minute = minute(sdate),
time = (hour + (minute/60)),
wday = wday(sdate, label = TRUE)) %>%
ggplot(aes(x = time))+
facet_wrap(vars(wday))+
geom_density()+
labs(title = "Distribution of Bike Rentals by Time of Day",
x = "",
y = "")
This data plot combines two of the plots above and shows on each individual day the distribution of when rentals are purchased on that given day. Saturday and Sunday just have one spike which is around Midday and each weekday there is two spikes one for the morning rush and one for the afternoon rush for people on the way home.
The variable client describes whether the renter is a regular user (level Registered) or has not joined the bike-rental organization (Causal). The next set of exercises investigate whether these two different categories of users show different rental behavior and how client interacts with the patterns you found in the previous exercises.
fill aesthetic for geom_density() to the client variable. You should also set alpha = .5 for transparency and color=NA to suppress the outline of the density function.Trips %>%
mutate(hour = hour(sdate),
minute = minute(sdate),
time = (hour + (minute/60)),
wday = wday(sdate, label = TRUE)) %>%
ggplot(aes(x = time, fill = client))+
facet_wrap(vars(wday))+
geom_density(alpha = .5)+
labs(title = "Distribution of Bike Rentals by Time of Day
and Type of Client",
x = "",
y = "")
position = position_stack() to geom_density(). In your opinion, is this better or worse in terms of telling a story? What are the advantages/disadvantages of each?Trips %>%
mutate(hour = hour(sdate),
minute = minute(sdate),
time = (hour + (minute/60)),
wday = wday(sdate, label = TRUE)) %>%
ggplot(aes(x = time, fill = client))+
facet_wrap(vars(wday))+
geom_density(alpha = .5, position = position_stack())+
labs(title = "Distribution of Bike Rentals by Time of Day
and Type of Client",
x = "",
y = "")
This is much better with making conclusions because there is less overlapping and more definitive lines. Overall, it tells a much better story in a much clearer way.
position = position_stack()). Add a new variable to the dataset called weekend which will be “weekend” if the day is Saturday or Sunday and “weekday” otherwise (HINT: use the ifelse() function and the wday() function from lubridate). Then, update the graph from the previous problem by faceting on the new weekend variable.Trips %>%
mutate(hour = hour(sdate),
minute = minute(sdate),
time = (hour + (minute/60)),
day_of_week = wday(sdate, label = TRUE),
day_type = ifelse(wday(sdate) %in% c(1,7), "weekend", "weekday")) %>%
ggplot(aes(x = time, fill = client))+
facet_wrap(vars(day_type))+
geom_density(alpha = .5,)+
labs(title = "Distribution of Bike Rentals by Time of Day, Type of Day,
and Type of Client",
x = "",
y = "")
client and fill with weekday. What information does this graph tell you that the previous didn’t? Is one graph better than the other?Trips %>%
mutate(hour = hour(sdate),
minute = minute(sdate),
time = (hour + (minute/60)),
day_of_week = wday(sdate, label = TRUE),
day_type = ifelse(wday(sdate) %in% c(1,7), "weekend", "weekday")) %>%
ggplot(aes(x = time, fill = day_type))+
facet_wrap(vars(client))+
geom_density(alpha = .5,)+
labs(title = "Distribution of Bike Rentals by Time of Day, Type of Day,
and Type of Client",
x = "",
y = "")
This graph facets on clients and fills like weekday unlike the other. Its clear in both to see the density distributions.
Stations to make a visualization of the total number of departures from each station in the Trips data. Use either color or size to show the variation in number of departures. We will improve this plot next week when we learn about maps!Trips %>%
count(sstation) %>%
inner_join(Stations,
by = c("sstation" = "name")) %>%
ggplot(aes(x = long, y = lat, color = n))+
geom_point()+
labs(title = "Total Number of Departures From Each Station
by Latitude and Longitude",
x = "longitude",
y = "latitude")
Trips %>%
group_by(sstation) %>%
summarize(tot_dept = n(),
prop_casual = mean(client == "Casual")) %>%
left_join(Stations,
by = c("sstation" = "name")) %>%
ggplot(aes(x = long, y = lat, color = prop_casual))+
geom_point()+
labs(title = "Areas With Stations with a Higher %
of Departures by Casual Users by Latitude and Longitude",
x = "longitude",
y = "latitude")
There is a main cluster of points between -77.1 and -77.0 that ranges from 38.8 and 39.0. There is also a tiny cluster around 39.1 and around -77.15. Finally due to their shades of blue their proportion is less than 0.3.
as_date(sdate) converts sdate from date-time format to date format.top_trip <- Trips %>%
mutate(sdate = as_date(sdate)) %>%
count(sstation, sdate) %>%
slice_max(n = 10, order_by = n, with_ties = FALSE)
top_trip
Trips %>%
mutate(sdate = as_date(sdate)) %>%
inner_join(top_trip,
by = c("sstation", "sdate"))
Trips %>%
mutate(sdate = as_date(sdate)) %>%
inner_join(top_trip, by = c("sstation", "sdate")) %>%
mutate(day_of_week = wday(sdate, label = TRUE)) %>%
group_by(client, day_of_week) %>%
summarize(trips_day = n()) %>%
group_by(client) %>%
mutate(prop = trips_day/sum(trips_day)) %>%
pivot_wider(id_cols = day_of_week,
names_from = client,
values_from = prop)
For days of the weekend, the proportion of casual riders is greater than the proportion of registered riders. On weekdays except for Friday and Monday, which isn’t measured above, the proportion of registered is greater. This can be explained by regular work commuters during the week compared recreational fun on the weekends.
DID YOU REMEMBER TO GO BACK AND CHANGE THIS SET OF EXERCISES TO THE LARGER DATASET? IF NOT, DO THAT NOW.
This problem uses the data from the Tidy Tuesday competition this week, kids. If you need to refresh your memory on the data, read about it here.
facet_geo(). The graphic won’t load below since it came from a location on my computer. So, you’ll have to reference the original html on the moodle page to see it.I have no clue very sorry.
DID YOU REMEMBER TO UNCOMMENT THE OPTIONS AT THE TOP?